Seven Opportunities for Artificial Intelligence in Primary Care Electronic Visits: Qualitative Study of Staff and Patient Views

PLAIN-LANGUAGE SUMMARY

Original Research 

Patients and Staff Identify Seven Opportunities for Artificial Intelligence To Decrease Staff Workload and Improve Patient Safety in Primary Care eVisits 

Background and Goal: Although remote or electronic visits (eVisits) can increase access to health care for certain groups of patients, their use can increase staff workload and patient demand. Artificial intelligence (AI) may mitigate these outcomes. This study explored the views of staff and patients in primary care to inform the development of artificial intelligence (AI) features for eVisits.

Study Approach:Researchers conducted interviews and focus groups with 16 primary care staff and 37 patients from 14 primary care practices in northwest England and London. Interviewees were asked about their views on the potential uses of AI during eVisits, risks, benefits, and likely challenges to its adoption into clinical practice. Transcripts were thematically analyzed to identify key themes.

Main Results:         

• Initial misconceptions and reservations: both groups were unsure what AI could or could not do; patients worried it might diagnose or prescribe without input from their physician, and staff questioned safety.

• Perceived benefits included faster responses for patients and lighter workload for staff if AI handled routine tasks. Perceived risks included depersonalised care, data‑privacy fears, and the possibility that patients would have to enter symptoms perfectly for AI triage to work safely.

Seven specific opportunities for AI during eVisits were identified and generally welcomed if they complemented (not replaced) clinician judgment: 

• Workflow routing – AI could direct each request to the appropriate team member quickly.

• Directing – It could reroute emergencies to emergency services and send non-urgent issues to pharmacies.

• Prioritization – Urgent requests could be flagged so clinicians see them first.

• Follow-up questions – AI could automatically request photos, questionnaires, or clarification after a submission.

• Writing assistance – It could suggest editable response templates for common concerns like mental health.

• Self-help information – Trusted educational links could be sent to patients without clinician effort.

• Face-to-face booking – AI could automatically schedule in-person visits when a physical exam is likely needed.

Why It Matters: These findings highlight seven AI opportunities, identified by patients and staff, that could decrease staff workload and improve patient safety. The results of this study may serve as guidance for developing and testing AI tools in primary care settings.

Seven Opportunities for Artificial Intelligence in Primary Care Electronic Visits: Qualitative Study of Staff and Patient Views

Susan Moschogianis, PhD, et al

School of Health Sciences, Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester, United Kingdom 

Visual Abstract